דרושים » מוצר » מנהל /ת פרויקטי תשתית דאטה בענן

We're looking for a Staff-level Senior PM that will strategically define and execute the data ecosystem vision, focusing heavily on enabling self-service data consumption across the organization.
You'll bridge the gap between Data and Risk analysts, Data Engineering, Data Science, and business units, ensuring our data assets are high-quality, discoverable, and packaged as successful products that unlock measurable business value.
This role offers the unique opportunity to shape the core data infrastructure powering our next generation of products while working with cutting-edge technology and a team dedicated to innovation.
Responsibilities
Product Strategy and Vision: define and champion the product vision and roadmap for our data products (e.g., internal analytics platforms, external data APIs, machine learning model outputs, core data pipelines).
Stakeholder Collaboration: Serve as the primary liaison between data scientists, data engineers, software engineers, and business stakeholders to gather requirements, prioritize features, and manage expectations.
Requirements Definition: Translate complex business problems and user needs into clear, actionable user stories, functional specifications, and acceptance criteria for data products.
Self-Service Enablement: design and optimize data products to maximize self-service consumption. This includes curating accessible datasets, developing intuitive interfaces, and ensuring data assets are well-documented and easily discoverable by business users.
Data Tool Ownership: Drive the adoption, feature roadmap, and success of our core services; empower analysts and business users with direct data access.
Prioritization and Roadmap Management: Manage the backlog for data product development, making tough trade-off decisions based on business impact, technical feasibility, and strategic alignment.
Product Launch and Adoption: plan and execute successful product launches, including go-to-market strategies, documentation, and driving adoption of data products across the organization.
Performance Measurement: Define, track, and analyze key performance indicators (KPIs) for data products (e.g., data quality, latency, usage, business impact, cost) to ensure product success and inform iterations.